Customized middleware experience in a tertiary care hospital hematology laboratory
Bibliographic record
Abstract
Background: In the clinical laboratory, middleware is a software application that sits between the analyzer and the laboratory information system (LIS). One of the more common uses of middleware is to perform more efficient result autoverification than can be achieved by the LIS or analyzer alone. In addition to autoverification, middleware can support highly customized rules to handle samples and results from specific patient locations. The objective of this study was to review the impact of customized middleware rules that were designed and implemented in the hematology laboratory of a 1000-bed tertiary care adult academic center hospital. Methods: Three novel initiatives using middleware rules to achieve workflow efficiencies were retrospectively reviewed over different audit periods: preliminary neutrophil resulting for oncology patients, microcytosis interpretive comments, and 1 white blood cell differential (WBCD) reported per day. In addition, autoverification rates for complete blood count and differential (CBCD) and coagulation tests were calculated. Results: A preliminary neutrophil count was released from middleware on average 64 min before the final CBCD for Leukemia/Bone Marrow Transplant (L/BMT) outpatients, and on average 59 min earlier for oncology patients. Reflexing interpretive comments for select instances of microcytosis removed on average 500 slides per month from technologist review with an estimated cost savings of approximately $3383.33 CAD per month. The 1 WBCD per day rule resulted in a 5.1% cancelation rate, resulting in an estimated monthly cost savings of $943.46 CAD in reagents and technologist time. Finally, middleware rules achieved very high autoverification rates of 97.2% and 88.3% for CBC and CBCD results, respectively. Conclusions: Implementation of customized middleware hematology rules in our institution resulted in multiple positive impacts on workflow, achieving high autoverification rates, reduced slide reviews, cost savings, and improved standardization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".